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Found 12 papers, 5 papers with code
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce.
Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive.
When and how epochwise double descent happens
This model is based on the hypothesis that the training data contains features that are slow to learn but informative.
On the geometry of generalization and memorization in deep neural networks
Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance.
Emergence of Separable Manifolds in Deep Language Representations
In addition, we find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds' radius, dimensionality and inter-manifold correlations.
Untangling in Invariant Speech Recognition
Higher level concepts such as parts-of-speech and context dependence also emerge in the later layers of the network.
Semi-supervised voice conversion with amortized variational inference
In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker.
Probing emergent geometry in speech models via replica theory
The success of deep neural networks in visual tasks have motivated recent theoretical and empirical work to understand how these networks operate.
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set.
Deep Speech Denoising with Vector Space Projections
We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise.
Monaural Audio Speaker Separation with Source Contrastive Estimation
Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference.
